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get_pending_approvals

Get CONTENT PIPELINE outputs waiting for approval/publish (changelogs, newsletters, social drafts). IDs are pipeline_outputs UUIDs — use approve_pipeline_item / publish_pipeline_item / request_content_revision. NOT Command Center decision cards — those use get_command_center_items + get_command_center_item + decide_command_center_item. Use when user asks "what content needs my review?", "ready to publish?", or "approval queue" for content.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum items to return (default: 10)
companyIdYesFreedomOS company id to act within (you must be a member). Required for company-scoped tools.

TDQS

A4.2/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must disclose behavioral traits. It mentions IDs are pipeline_outputs UUIDs and implies subsequent actions, but does not explicitly state that this is a read-only operation or describe any pagination, rate limits, or ordering. The description provides minimal additional behavioral context beyond the obvious list retrieval.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two concise sentences with no wasted words. The first sentence states the core purpose and examples; the second provides usage alternatives and clarifying examples. It is well-front-loaded and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 2 well-documented parameters and no output schema, the description covers the essentials: what items are returned, how to act on them, and differentiation from sibling tools. It lacks details on ordering, pagination, or returned fields, but for a simple list tool with good sibling differentiation, it is largely complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with both parameters described. The description adds no extra meaning to the parameters; it only mentions that IDs are pipeline_outputs UUIDs, which relates to return values not parameters. Thus, the description does not significantly augment the parameter schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves 'CONTENT PIPELINE outputs waiting for approval/publish' and lists specific examples like changelogs, newsletters, and social drafts. It explicitly distinguishes from Command Center decision cards by naming alternative tools, ensuring no confusion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit when-to-use guidance: 'when user asks "what content needs my review?", "ready to publish?", or "approval queue" for content.' It also gives a clear when-not-to-use by stating 'NOT Command Center decision cards' and directing to the appropriate tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.6/5.0
Disambiguation4/5

The tool set is heavily disambiguated by detailed routing descriptions, domain prefixes, and lifecycle verbs, so most tools have a clear intended purpose. However, at 297 tools there are still close pairs and overlapping decision surfaces (e.g., approval workflows, 'what should I work on' readers, multiple finance/ads readers) that require careful description reading to avoid misselection.

Naming Consistency4/5

Naming is predominantly consistent snake_case verb_noun with strong domain prefixes like shopify_, x_, posthog_, and list_/create_/update_ patterns. Minor inconsistencies exist, such as several collection-returning tools using get_ (get_team_members, get_icps, get_okrs) instead of list_, and some generate_ vs create_ vs draft_ verbs, but the pattern is still predictable overall.

Tool Count1/5

297 tools is an extreme outlier and far beyond a usable MCP tool surface. Even a large suite has no justification for this count in one server; the agent would struggle to select among hundreds of similarly descriptive tools, and the natural 3-15 tool range is exceeded by nearly 20x.

Completeness4/5

The individual domains represented — OKRs, CRM/leads, Shopify, content pipelines, ads, PostHog, team hiring, knowledge, finance, and session management — are covered remarkably well with full lifecycle patterns. Minor gaps exist, such as no full deal CRUD, no delete for several Google/Shopify artifacts, and some analytical surfaces being read-heavy, but most workflows can be completed without dead ends.

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